Simultaneous determination of phorate and oxyfluorfen in well water samples with high accuracy by gc-ms after binary dispersive liquid-liquid microextraction.

Simultaneous Determination of Phorate and Oxyfluorfen

in Well Water Samples with High Accuracy by GC-MS After

Binary Dispersive Liquid-Liquid Microextraction

Emel Alkan&Fatih Kapukıran&Elif Öztürk Er& Dotse Selali Chormey&Seyfullah Keyf&

Nizamettin Özdoğan&Sezgin Bakırdere

Received: 19 June 2018 / Accepted: 31 July 2018 / Published online: 18 August 2018 # Springer Nature Switzerland AG 2018

Abstract The potential risk of pesticides to cause harm to humans and other organisms even at trace levels calls for sensitive and accurate analytical techniques for their simultaneous qualitative and quantitative determina-tions. In this study, a sensitive binary dispersive liquid-liquid microextraction (B-DLLME) strategy was devel-oped for the simultaneous determination of phorate and oxyfluorfen by gas chromatography mass spectrometry after extraction/preconcentration from aqueous solution. An experimental design was used to optimize parame-ters of the B-DLLME method to obtain maximum out-come. Under the optimum conditions of B-DLLME, the limit of detection (LOD) for phorate and oxyfluorfen were found to be 0.41μg L−1and 0.54μg L−1, respec-tively. The detection limits correlate to about 37 and 73 folds enhancement in detection powers when compared to direct GC-MS determination of phorate and oxyfluorfen, respectively. In order to find out the appli-cability of developed method to real samples, recovery tests were performed for 20 μg L−1 of phorate and

oxyfluorfen spiked in well water samples. Percent re-covery values were found to be 94.5% for phorate and 101.9% for oxyfluorfen.

Keywords Pesticides . Phorate . Oxyfluorfen . GC-MS . DLLME . B-DLLME

1 Introduction

Pesticides have been widely used throughout the world owing to their high activity on repelling, controlling, and eliminating pests in different fields such as agri-culture, commerce, and households (Alavanja et al. 2004). However, the growing uses of pesticides bring about negative externalities including adverse envi-ronmental and human health consequences (Abhilash and Singh2009). The occurrence of pesticides and their metabolites in the ecosystem affects and pollutes every component of the environment (rivers, soil, sed-iment, surface, and ground water) (Abhilash and Singh 2009). This contamination poses a great risk to human health since residues in food and drinking water can enter the body and accumulate in organs and tissues (Wilson and Tisdell2001).

Among the various pesticide types, oxyfluorfen (2-c h l o r o -α,α,α-trifluoro-p-tolyl 3-ethoxy-4-n i t r o p h e 3-ethoxy-4-n y l e t h e r ) a 3-ethoxy-4-n d p h o r a t e ( o , o - d i e t h y l s-[(ethylthio)methyl] dithiophosphate) are two of the widely used chemicals with high toxicities even at trace levels (Hassanein 2002; Saquib et al. 2012). Oxyfluorfen is a herbicide commonly used to control

https://doi.org/10.1007/s11270-018-3939-2

E. Alkan

:

:

Chemical Engineering Department, Yıldız Technical University, 34210İstanbul, Turkey

e-mail: [email protected] F. Kapukıran

:

Institute of Science, Environmental Engineer Department, Bülent Ecevit University, 67100 Zonguldak, Turkey

D. S. Chormey

:

Faculty of Art and Science, Chemistry Department, Yıldız Technical University, 34210İstanbul, Turkey

broadleaf and grassy weeds (Roberts and Croucher 2007) while phorate is primarily used to control in-sects, leafhoppers, and leaf miners (Hayes and Laws 1991). The persistent use of both pesticides may even-tually find its way into humans and cause carcinogenic, mutagenic, or genotoxic effects (Mahajan et al.2006; Goldman1998). Despite the different regulations and constraints that limit the use of these agrochemicals, regular monitoring of their levels in foods and environ-mental samples is still needed to maintain food quality and ensure public health (Masiá et al.2014). In this sense, several analytical methods have been developed for the detection and quantification of oxyfluorfen (Wu et al.2013; Djurovic et al.2015) and phorate (Ahmadi et al.2006; Xiao et al.2006) by using different conven-tional chromatographic techniques such as high per-formance liquid chromatography (HPLC) (Peruzzi et al.2000), liquid chromatography-mass spectrome-try (LC-MS) (Fillion et al.2000), gas chromatography-flame photometric detection (GC-FPD) (Khalili-Zanjani et al.2008), GC-electron capture detection (GC-ECD) (Jiménez et al. 1998), GC-nitrogen-phosphorus detection (GC-NPD) (Fenoll et al.2007), and GC-mass spectrometry (GC-MS) (Nguyen et al. 2010; Zambonin et al.2004). These instrumental tech-niques are usually combined with various sample treat-ment methods to extract/enrich the target analyte(s) from complex matrices. To date, single drop microextraction (SDME) (Ahmadi et al. 2006; Xiao et al. 2006), solid phase extraction (SPE) (Miliadis 1994; Djurovic et al. 2015), cloud point extraction (CPE) (He et al.2010), stir bar sorptive extraction (Yu a n d H u 2 0 0 9) , a n d d i s p e r s i v e l i q u i d - l i q u i d microextraction (DLLME) (Berijani et al.2006) ap-proaches have been applied for the extraction/ preconcentration of oxyfluorfen and phorate from var-ious matrices including biological, environmental, and food samples. DLLME is an efficient technique for sample treatment due to easy and fast operation, high extraction capacity, and low solvent consumption (Zgoła-Grześkowiak and Grześkowiak 2011). This method requires an extraction solvent with density higher or lower than water and immiscible in water, and a dispersive solvent that is miscible with both extraction solvent and water (Xiao-Huan et al.2009). An appropriate mixture of extraction and dispersive solvent is rapidly injected to the sample solution resulting in the formation of a cloudy solution (Rezaee et al. 2006). The extraction efficiency of

DLLME process significantly depends on the interac-tion of the compounds of interest and extracinterac-tion sol-vent (Xiao-Huan et al. 2009). Thus, in simultaneous determinations, an extraction solvent might efficient-ly extract some anaefficient-lytes while others record low traction efficiencies. Herein, binary mixtures of ex-traction solvents can be used to improve the exex-traction efficiencies of compounds being determined at the same time.

In this study, the main aim was to develop a sensitive and accurate B-DLLME method for the simultaneous determination of oxyfluorfen and phorate at trace levels. The B-DLLME method was combined with GC-MS technique, and the optimum method was applied to well water sample.

2 Materials and Methods

2.1 Reagents

Phorate and oxyfluorfen (high purity > 98%) were pur-chased from Dr. Ehrenstorfer GmbH (Augsburg, Ger-many). All other chemicals used for sample/standard preparation, optimization, and extraction procedures were obtained from Merck (Darmstadt, Germany). Ace-tonitrile was used to prepare 1000 mg L−1stock standard solution of two analytes, from which appropriate ali-quots were diluted to prepare calibration and working standard solutions. All sample/standard preparations were made volumetrically. Optimization of B-DLLME procedure was performed by using acetone, acetonitrile, ethanol, 2-propanol, dichloromethane, 1,2-dichloroeth-ane, chloroform, carbon tetrachloride, barium chloride, potassium chloride, potassium nitrate, and sodium chlo-ride. The ultrapure water used for dilution and cleaning purposes was obtained from a Milli-Q Reference Sys-tem (18.2 MΩ.cm resistance).

2.2 Instrument

Chromatographic separation of phorate and oxyfluorfen was achieved with a 30-m-long HP-5MS capillary col-umn (0.25-mm narrowbore and 0.25-μm film) connect-ed to an Agilent 6890 GC gas chromatograph and a mass spectrometer. All sample/standard injections were run in splitless mode using helium as a carrier gas at 1.8 mL min−1 constant flow rate. Injection port and auxiliary temperatures were 250 °C and 280 °C,

respectively. The temperature program employed for analyte separation was as follows: 30 °C min−1increase from an initial 70 to 210 °C, and a final ramp of 50 °C min−1 from 210 to 280 °C. The quantifier/ qualifier ions extracted from total ion chromatograms for phorate and oxyfluorfen were m/z 75/121 and m/z 252/361, respectively.

2.3 Procedure

1.0 g of potassium nitrate was dissolved in 8.0-mL standard/sample solution in a 15-mL conical centrifuge tube using vortex for rapid dissolution. A mixture of 2-propanol (2.0 mL) as a dispersive solvent and 200-μL binary solvent (100-μL 1,2- dichloromethane and 100-μL dichloroethane) were mixed in a different tube, taken with a syringe and injected into the salt solution. After the injection, the resulted turbid solution was vortexed for 15 s and then centrifuged at 6000 rpm for 120 s to quicken phase separation. Approximately, 55μL of the binary solvent bottom phase was drawn and placed into micro-volume insert glass vials for GC-MS automatic injections.

2.4 Samples

Well water was sampled into clean polypropylene plas-tic containers and stored in a cool dry cabinet in the laboratory before use. The water sample was filtered to remove solid particles before analysis.

3 Results and Discussions

Prior to B-DLLME method development, the analytical performance of direct GC-MS determination of phorate and oxyfluorfen was determined with mixed standard solutions prepared between 0.10 and 100 mg L−1. Both analytes showed good linearity (R2> 0.9992) between 0.10 to 20 mg L−1, and the detection limits were calcu-lated as 0.03 mg L−1 and 0.02 mg L−1 phorate and oxyfluorfen, respectively. Experimental parameters were selected by univariate optimizations, and the pa-rameters selected were fitted into the experimental de-sign to determine their optimum amounts.

3.1 Determination of Experimental Variables

In order to obtain high extraction efficiency for the analytes, only the most effective extraction parameters were added to the experimental design. These parame-ters were chosen by performing step-by-step optimiza-tions and selecting the highest mean peak area from three replicate measurements. The first parameter select-ed was the type of extraction solvent, and this includselect-ed organic solvents with densities greater than water and their combinations (binary mixtures). Using ethanol as dispersive solvent, chloroform, dichloromethane, car-bon tetrachloride, and 1,2-dichloroethane together with the binary mixture of each two solvents were used to extract analytes (50 μg L−1) from aqueous standards, and the results obtained are shown in Fig.1.

Dichloromethane is not shown in the figure because it did not produce a separated phase after extraction, but its binary mixtures recorded high peak area values. The highest result was obtained for the 1,2-dichloroethane (DCE) and dichloromethane (DCM) mixture. Acetone, acetonitrile, and 2-propanol were tested in addition to ethanol to determine the optimum dispersion efficiency for the DCE/DCM binary mixture. The peak area values recorded for ethanol and 2-propanol were significantly higher (≈ 3 times) than acetone, and acetonitrile. 2-propanol was selected over ethanol due to more repeat-able results. Salting out effect was also tested on the analytes using 1.0 g each of potassium nitrate, barium chloride, sodium chloride, and potassium chloride. The salts were dissolved in equivalent standard solutions and extracted with the 2-propanol/DCE/DCM mixture. The salt added extractions were performed alongside a salt-less extraction, and the results obtained for potassium nitrate were higher than the others. The parameters selected for the experimental design were DCE/DCM binary mixture (A), ethanol (B), and potassium nitrate (C). Mixing by vortex for 15 s was held constant for the experiments.

3.2 Experimental Design and Optimization of the Extraction Process

The B-DLLME parameters were optimized by a Box-Behnken design (Design Expert 7.0.0 software), which is a multi-variable experimental design used to deter-mine the most suitable test conditions for a process. The

advantage of the Box-Behnken design is that the designs are spherical and can be rotated, and the factors are carried out at only three levels. The three factors (A, B, and C) were set at three levels (+ 1, 0,− 1) in the Box-Behnken design which comprised of 17 combination studies with five randomly distributed centers.

The quadratic polynomial expression of Eq. 1 was used to define the pattern of the three independent variables (X1, X2,and X3) (Lazic2006).

Y ¼ β0þ β1X1þ β2X2þ β3X3þ β12X1X2 þ β13X1X3þ β23X2X3þ β11X12þ β22X22

þ β33X32 ð1Þ

Y in the polynomial expression is the response (peak area), and other parameters include a constantβ0, linear coefficientsβ1,β2, andβ3, interaction coefficientsβ12, β13, andβ23, and quadratic coefficientsβ11,β22, andβ33. The average responses and the design of experimental combinations were applied to analysis of variance (ANOVA) to determine significant variables for the mod-el. The expression below renders the empirical relation-ship between the variables and the responses for phorate and oxyfluorfen given in Eqs.2and3, respectively;

R1¼ 2467420− 10487:78736  Að Þ

þ 238313  Bð Þ− 557481  Cð Þ− 805:8125  A  Bð Þ þ 1337:87667  A  Cð Þ þ 13:05398  A2

ð2Þ

Table 1 Results of the ANOVA

obtained for phorate Source Sum of squares Degree of freedom Mean squares F value P value Model 6.463E + 011 6 1.077E + 011 69.94 < 0.0001 A-binary 5.148E + 011 1 5.148E + 011 334.23 < 0.0001 B-2-propanol 7.060E + 007 1 7.060E + 007 0.046 0.8352 C-KNO3 4.875E + 010 1 4.875E + 010 31.65 0.0003

AB 1.558E + 010 1 1.558E + 010 10.12 0.0112 AC 1.790E + 010 1 1.790E + 010 11.62 0.0078 A2 _{6.1350E + 010 1 6.135E + 010 39.83 0.0001}

Residual 1.368E + 010 9 1.540E + 009

Lack of fit 1.012E + 010 5 2.024E + 009 2.16 0.2372 Pure error 3.741E + 009 4 9.353E + 008

Cor total 6.602E + 011 15 R2 _0.9790

R1¼ 1765880− 8896:7495  Að Þ

þ 264910  Bð Þ− 329120  Cð Þ− 410:375  A  Bð Þ þ 1149:76667  A  Cð Þ− 45896:33333  B  Cð Þ

þ 10:95182  A2
_{− 22549:13333  B}2
_{− 14660:5333  C}2

ð3Þ A summary of ANOVA results of phorate is pre-sented in Table1 as an example to show significant values. Based on the ANOVA data, the P values are used to determine whether the variables and their interactions with each other are meaningful or not. F values indicate the value of each variable and its effect

on the variance response. P values less than 0.05 indicate that the model term is meaningful. According to this, the amount of binary solvent and salt amount were the most influential main variables. The interac-tion between amount of binary solvent and amount of dispersive solvent (AB) and between amount of binary solvent and amount of salt (AC) were significant mod-el parameters. A response surface plot generated from Eq.1 for the AC interaction for phorate is shown in Fig.2. The amounts of binary solvent and potassium nitrate showed negative main effects, where their lowest amounts gave the highest peak area values. Low amounts of extraction solvents result in high

Fig. 2 A 3D response surface showing the effects of extraction solvent volume and salt amount on the response of phorate

Fig. 3 Normal probability plot of phorate

preconcentration factors; and the amount of salt added should be enough to cause the salting out effect, but excess amounts could alter the density of solution and hinder analyte extraction. Although the amount of 2-propanol (B) was not statistically significant, it was observed to have a positive effect. As a result, opti-mum experimental conditions were determined as 2.0-mL 2-propanol, 0.50-g potassium nitrate, and 200-μL binary solvent (100 μL DCE + 100 μL DCM). The F value and P value of the proposed model are 69.94 and < 0.0001, respectively. The nor-mal probability graph shows the regression coeffi-cient (R2) for the straight line shown in Fig. 3 as 98%. It is seen that the expected values are linear with the experimental data. The adjusted R2value of 97% shows an error margin of 1.0%. Thus, the model is accurate and reliable, validating the predictions made for optimum experimental conditions.

3.3 Analytical Figures of Merit

Optimum conditions of B-DLLME from the experimen-tal design were applied to aqueous standard solutions between 0.50 and 1000μg L−1to determine the analyt-ical performance for phorate and oxyfluorfen as present-ed in Table2. The limit of detection (LOD) was calcu-lated as 3 times the standard deviation (SD) of the lowest calibration concentration divided by the slope (m) of calibration plot. The quantification limit (LOQ) was calculated as 10 × SD/m. Other figures of merit included regression coefficient (R2) and percent relative standard deviation (%, RSD for the lowest standard in the cali-bration plot) are given in Table2. The LOD/LOQ values calculated for phorate and oxyfluorfen were 0.41/ 1.37 μg L−1 and 0.54/1.82 μg L−1, respectively. In comparison to the detection limits obtained by direct GC-MS, the B-DLLME method recorded enhancement in detection powers by 37 and 73 folds for phorate and oxyfluorfen, respectively. The figures of merit obtained for the B-DLLME method were satisfactory.

3.4 Recovery Studies in Well Water

The developed B-DLLME method was applied to real time samples to determine the amount of phorate and oxyfluorfen when present in the sample matrix. Due to the complex matrix of most environmental and biolog-ical samples, extraction of analytes can be greatly hin-dered leading to low recovery results (Koçoğlu et al. 2017). For this reason, the developed method was tested on a well water sample. Blank analysis was performed on the sample to ensure the analytes were not present or to use the detected amount for blank correction. There were no signals observed at the retention times of the analytes for the blank measurement, and the sample was then spiked at 20 and 100μg L−1. The spiked samples were extracted under the optimum conditions and the percentage recoveries shown in Table3were calculated from a calibration plot developed with standard solu-tions prepared in deionized water. Results obtained for the two analytes ranged between 94.5 and 106.8%. The closeness of the recovery results to 100% shows that the B-DLLME-GC-MS method can be used to accurately determine phorate and oxyfluorfen in well water matrix.

4 Conclusion

In this study, an accurate and sensitive analytical method namely B-DLLME-GC-MS was developed for the si-multaneous determination of oxyfluorfen and phorate. Dispersive liquid-liquid microextraction based on a bi-nary mixture of two extraction solvents was used to preconcentrate the analytes from aqueous solution. Box-Behnken experimental design was used to study the effect of main experimental parameters and their interactions with each other, and the design model used to predict optimum experimental values. Optimum ex-perimental conditions were used to determine analytical figures of merit for the analytes, and about 37 and 73 fold enhancements in detection powers were obtained for phorate and oxyfluorfen, respectively. The percent relative standard deviation calculated for the lowest

Table 2 Analytical figures of merit of analytes obtained using the optimized B-DLLME method

Chemical LOD,μg L−1 LOQ,μg L−1 % RSD R2 Phorate 0.41 1.37 10.83 0.9996 Oxyfluorfen 0.54 1.82 9.96 0.9992

Table 3 Percent recovery results for phorate and oxyfluorfen Analyte 20μg L−1(%) 100μg L−1(%) Phorate 94.5 ± 13.8 98.8 ± 8.0 Oxyfluorfen 101.9 ± 7.6 106.8 ± 4.6

calibration concentration of both analytes was low, sig-nifying good precision for the extraction process and instrumental readings. The method was applied to well water samples to determine its accuracy and applicabil-ity to real samples. The percent recoveries obtained at two spiked concentrations were found to be very close to 100%, and this established that the developed method could be used for accurate quantification of the analytes in well water matrix. This method is simple, economi-cal, rapid, and agrees with green chemistry, requiring very little organic solvent, and is therefore suitable for routine laboratory analysis.

Compliance with Ethical Standards

Conflict of Interest The authors declare that they have no conflict of interest.

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